Dark soliton detection using persistent homology

Author:

Leykam Daniel1ORCID,Rondón Irving2ORCID,Angelakis Dimitris G.13ORCID

Affiliation:

1. Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543

2. School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegi-ro, Seoul 02455, Republic of Korea

3. School of Electrical and Computer Engineering, Technical University of Crete, Chania 73100, Greece

Abstract

Classifying images often requires manual identification of qualitative features. Machine learning approaches including convolutional neural networks can achieve accuracy comparable to human classifiers but require extensive data and computational resources to train. We show how a topological data analysis technique, persistent homology, can be used to rapidly and reliably identify qualitative features in experimental image data. The identified features can be used as inputs to simple supervised machine learning models, such as logistic regression models, which are easier to train. As an example, we consider the identification of dark solitons using a dataset of 6257 labeled atomic Bose–Einstein condensate density images.

Funder

National Research Foundation Singapore

National Research Foundation of Korea

Ministry of Education - Singapore

European Regional Development Fund

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

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